rsl_rl/rsl_rl/utils/wandb_utils.py

78 lines
2.4 KiB
Python

# Copyright 2021 ETH Zurich, NVIDIA CORPORATION
# SPDX-License-Identifier: BSD-3-Clause
from __future__ import annotations
import os
from dataclasses import asdict
from torch.utils.tensorboard import SummaryWriter
try:
import wandb
except ModuleNotFoundError:
raise ModuleNotFoundError("Wandb is required to log to Weights and Biases.")
class WandbSummaryWriter(SummaryWriter):
"""Summary writer for Weights and Biases."""
def __init__(self, log_dir: str, flush_secs: int, cfg):
super().__init__(log_dir, flush_secs)
try:
project = cfg["wandb_project"]
except KeyError:
raise KeyError("Please specify wandb_project in the runner config, e.g. legged_gym.")
try:
entity = os.environ["WANDB_USERNAME"]
except KeyError:
raise KeyError(
"Wandb username not found. Please run or add to ~/.bashrc: export WANDB_USERNAME=YOUR_USERNAME"
)
wandb.init(project=project, entity=entity)
# Change generated name to project-number format
wandb.run.name = project + wandb.run.name.split("-")[-1]
self.name_map = {
"Train/mean_reward/time": "Train/mean_reward_time",
"Train/mean_episode_length/time": "Train/mean_episode_length_time",
}
run_name = os.path.split(log_dir)[-1]
wandb.log({"log_dir": run_name})
def store_config(self, env_cfg, runner_cfg, alg_cfg, policy_cfg):
wandb.config.update({"runner_cfg": runner_cfg})
wandb.config.update({"policy_cfg": policy_cfg})
wandb.config.update({"alg_cfg": alg_cfg})
wandb.config.update({"env_cfg": asdict(env_cfg)})
def _map_path(self, path):
if path in self.name_map:
return self.name_map[path]
else:
return path
def add_scalar(self, tag, scalar_value, global_step=None, walltime=None, new_style=False):
super().add_scalar(
tag,
scalar_value,
global_step=global_step,
walltime=walltime,
new_style=new_style,
)
wandb.log({self._map_path(tag): scalar_value}, step=global_step)
def stop(self):
wandb.finish()
def log_config(self, env_cfg, runner_cfg, alg_cfg, policy_cfg):
self.store_config(env_cfg, runner_cfg, alg_cfg, policy_cfg)
def save_model(self, model_path, iter):
wandb.save(model_path)